Deformation produced by an earthquake has a wide variety of forms. Therefore, there are a variety of models for quantifying the amount of deformation observed on GNSS coordinate timeseries, each of them based on different assumptions about the underlying mechanism that generates the data. Hence, it is of interest to look for methods relying on minimal assumptions about the observed position series. In this work we propose a semiparametric method, based on adversarial learning, to perform inference of the permanent seismic deformation. The only assumption made is that the probability distributions of GNSS fixed-length coordinate series for periods with and without observable seismic deformation differs mainly in the permanent deformation, represented by an additive scaled heaviside function. A dataset based on the series of GNSS coordinates published by the Nevada Geodetic Laboratory was built, and an adversarial model was trained over this dataset. In order to train the algorithm, an initial labeling of the samples was conducted using time, position an magnitude information of seismic events from the USGS database. It was shown that learning was possible with the available real data, and multiple sanity checks were run, showing consistency of the offset estimations compared with a trajectory model based estimator and with published offsets for well studied events on the South American active margin. To assess the capabilities of the method in a more controlled environment, further experiments were conducted on synthetic data. Those experiments confirmed that the presence of postseismic transient signals does not impede learning. As a derivative, our proposal allows to refine imperfect initial estimations for the presence/absence of seismic deformation.
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